Tag: Graphics processing unit

Amazon just did a cluster Christmas present for us tech geek lizards- before Google could out doogle them with end of the Betas (cough- its on NDA)

Clusters used by Academic Departments now have a great chance to reduce cost without downsizing- but only if the CIO gets the email.

While Professor Goodnight of SAS / North Carolina University is still playing time sharing versus mind sharing games with analytical birdies – his 70 mill server farm set in Feb last is about to get ready

( I heard they got public subsidies for environment- but thats historic for SAS– taking public things private -right Prof as SAS itself began as a publicly funded project. and that was in the 1960s and they didnt even have no lobbyists as well. )

In realted R news, Dirk E has been thinking of a R HPC book without paying attention to Amazon but would now have to include Amazon

(he has been thinking of writing that book for 5 years, but hey he’s got a day job, consulting gigs with revo, photo ops at Google, a blog, packages to maintain without binaries, Dirk E we await thy book with bated holes.

Unique to Cluster Compute and Cluster GPU instances is the ability to group them into clusters of instances for use with HPC

applications. This is particularly valuable for those applications that rely on protocols like Message Passing Interface (MPI) for tightly coupled inter-node communication.

Cluster Compute and Cluster GPU instances function just like other Amazon EC2 instances but also offer the following features for optimal performance with HPC applications:

When run as a cluster of instances, they provide low latency, full bisection 10 Gbps bandwidth between instances. Cluster sizes up through and above 128 instances are supported.

Cluster Compute and Cluster GPU instances include the specific processor architecture in their definition to allow developers to tune their applications by compiling applications for that specific processor architecture in order to achieve optimal performance.

The Cluster Compute instance family currently contains a single instance type, the Cluster Compute Quadruple Extra Large with the following specifications:

One more addition to the GPU stack that adds up power when combined with CPU and GPUs. For numeric computing, it may be essential to have GPU- CPU mixed software as almost all hardware people now have offered GPU-CPU products. Maybe software companies can get inspired for new kind of GPU-CPU blade server software again.

But for “true” supercomputing applications, the SL390s G7 is the go-to server. Like its sibling, the SL390s comes with Xeon 5600 processors, but the option to pair the CPUs with up to three on-board NVIDIA “Fermi” 20-series GPUs puts a lot more floating point performance into this design. Customers can choose from either the M2050 or M2070 Tesla GPU modules, the only difference being the amount of graphics memory — 3 GB of GDDR5 for the M2050 versus 6 GB for the M2070. Each GPU module is served by its own PCIe Gen2 x16 channel in order to maximize bandwidth to the graphics chips. At the maximum configuration with all three Fermi GPUs and two Westmere CPUs, a single server delivers on the order of 1 teraflop of double precision performance. “So this is very much a server that has been designed for HPC,” said Turkel.

With GPUs on board, the SL390s fill out a 2U half-width tray, so up to four of these can be packed into a 4U SL6500 chassis. A CPU-only version is also available and takes up just half the space (half-width 1U), enabling twice as many Xeons to occupy the same chassis. This configuration will likely be the server of choice for the majority of HPC setups, given that GPGPU deployment is really just getting started. Pricing on the CPU-only model starts at $2,259.

And

, the ProLiant SL390s G7, provides more raw FLOPS per square inch than any server HP has delivered to date, and is the basis for the 2.4 petaflop TSUBAME 2.0 supercomputer currently being deployed at the Tokyo Institute of Technology.

Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.

The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

The gputools package by Buckner provides several common data-mining algorithms which are implemented using a mixture of nVidia‘s CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.

The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia’s CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.

The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.

The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).

The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).

I tried to search for SAS and GPU and SPSS and GPU but got nothing. Maybe they would do well to atleast test these alternative hardwares-

Also see Matlab on GPU comparison for the product Jacket vs Parallel Computing Toolbox